coder3101/Qwen3.5-27B-heretic
The coder3101/Qwen3.5-27B-heretic is a 27 billion parameter causal language model, a decensored version of Qwen/Qwen3.5-27B created using Heretic v1.2.0 with Magnitude-Preserving Orthogonal Ablation (MPOA). This model features a unified vision-language foundation, an efficient hybrid architecture, and scalable reinforcement learning generalization, supporting a native context length of 262,144 tokens. It excels in multimodal understanding, agentic capabilities, and multilingual tasks across 201 languages, making it suitable for complex reasoning, coding, and visual understanding applications.
Loading preview...
Model Overview
The coder3101/Qwen3.5-27B-heretic is a 27 billion parameter multimodal causal language model, derived from the Qwen/Qwen3.5-27B base model through decensoring using Heretic v1.2.0 with Magnitude-Preserving Orthogonal Ablation (MPOA). This modification significantly reduces refusals from 94/100 in the original model to 14/100, while maintaining a low KL divergence of 0.0653.
Key Capabilities
- Unified Vision-Language Foundation: Achieves strong performance across reasoning, coding, agents, and visual understanding benchmarks through early fusion training on multimodal tokens.
- Efficient Hybrid Architecture: Utilizes Gated Delta Networks and sparse Mixture-of-Experts for high-throughput inference with minimal latency.
- Scalable RL Generalization: Benefits from reinforcement learning scaled across million-agent environments for robust real-world adaptability.
- Global Linguistic Coverage: Supports 201 languages and dialects, enabling inclusive worldwide deployment.
- Extended Context Length: Natively handles up to 262,144 tokens, extensible to 1,010,000 tokens with YaRN scaling, crucial for long-horizon tasks.
- Agentic Usage: Excels in tool calling, with recommended integration via Qwen-Agent for building agent applications and Qwen Code for terminal-based coding tasks.
Good for
- Multimodal Applications: Ideal for tasks requiring both visual and linguistic understanding, including complex VQA, document analysis, and video comprehension.
- Agent Development: Its strong tool-calling capabilities make it well-suited for building sophisticated AI agents.
- Long Context Processing: Excellent for applications requiring analysis or generation over very long documents or conversations.
- Multilingual Use Cases: Supports a wide array of languages, making it versatile for global deployments.
- Unrestricted Content Generation: The decensored nature allows for broader content generation compared to the base model.